Method, device and permanent storage memory for detecting drilling and/or hydraulic fracturing of hydrocarbon wells

ABSTRACT

A method for detecting pad construction and/or drilling and/or for hydraulic fracturing of at least one hydrocarbon well, comprising the steps of: selecting at least one specified well location, obtaining at least one time series of top view images of the specified well location, in which each top view image has a date corresponding to a day of acquisition of the top view image, processing the time series of top view images to detect at least one top view image showing the apparition of a well pad and/or showing drilling activity and/or showing fracturing activity, exporting the date corresponding to the acquisition day of the top view image showing the apparition of the well pad and/or drilling activity and/or fracturing activity, providing, based on export date, an information of pad construction date and/or drilling starting date and/or fracturing starting date and/or a full production forecast for the specified well location.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to U.S. Provisional PatentApplication No. 62/423,106 filed on Nov. 16, 2016, the entire disclosureof which is incorporated herein by reference.

The present application also claims priority to U.S. Provisional PatentApplication No. 62/451,466 filed on Jan. 27, 2017, the entire disclosureof which is incorporated herein by reference.

BACKGROUND OF THE INVENTION

The invention concerns a method and device for detecting padconstruction and/or drilling and/or hydraulic fracturing of hydrocarbonwells.

The invention generally applies to oil wells and/or gas wells.

Prior to production, several steps are required to bring a well online.In the USA, a permit for drilling must be issued first. This permitgives the operator permission to drill at a certain place before anexpiration date. In order to proceed with the subsequent steps, a “wellpad” is then constructed at the location of the permit to enable for theuse of heavy machinery. A rig in then mobilized on the well pad toproceed with the drilling of the well. Otherwise, for unconventionalwells, there is an additional hydraulic fracturing (fracking) step. Bothdrilling and fracking activity have a timescale of a few days to severalweeks and involve heavy machinery.

Thus, knowing exactly when a well is drilled or fracked is a key factorto estimate its production time series. However almost all officialdatabases containing the reports of drilling and fracking activity arelagged. This lag can sometimes exceed a year for some wells.

SUMMARY OF THE INVENTION

The goal of the invention is to obtain a method and apparatus fordetecting drilling and/or hydraulic fracturing of hydrocarbon wells,with a significantly lower lag using images to detect drilling orfracking activity.

According to one aspect of the invention, there is provided a method fordetecting pad construction for at least one hydrocarbon well and/or fordetecting drilling of at least one hydrocarbon well and/or for detectinghydraulic fracturing of at least one hydrocarbon well, comprising:

a step of selecting at least one specified well location,

a step of obtaining at least one time series of top view images of thespecified well location, in which each top view image is associated witha date corresponding to a day of acquisition of the top view image,

a step of processing the time series of top view images to detect atleast one top view image showing the apparition of a well pad and/orshowing drilling activity and/or showing fracturing activity,

a step of exporting the date corresponding to the day of acquisition ofthe top view image showing the apparition of the well pad and/or showingdrilling activity and/or showing fracturing activity,

a step of providing, based on the date having been exported, aninformation of pad construction date and/or of drilling starting dateand/or of fracturing starting date and/or a full production forecast forthe specified well location.

According to another aspect of the invention, there is provided a devicefor detecting pad construction for at least one hydrocarbon well and/orfor detecting drilling of at least one hydrocarbon well and/or fordetecting hydraulic fracturing of at least one hydrocarbon well,comprising:

a selector module for selecting at least one specified well location,

an image production module for obtaining at least one time series of topview images of the specified well location, in which each top view imageis associated with a date corresponding to a day of acquisition of thetop view image,

a processing module for processing the time series of top view images todetect at least one top view image showing the apparition of a well padand/or showing drilling activity and/or showing hydraulic fracturing,

an export module for exporting the date corresponding to the day ofacquisition of the top view image showing the apparition of the well padand/or showing drilling activity and/or showing hydraulic fracturing,

an information outputting module for providing, based on the date havingbeen exported, an information of pad construction date and/or ofdrilling starting date and/or of fracturing starting date and/or a fullproduction forecast for the specified well location.

According to another aspect of the invention, there is provided apermanent storage memory for storing a computer program for detectingpad construction for at least one hydrocarbon well and/or for detectingdrilling of at least one hydrocarbon well and/or for detecting hydraulicfracturing of at least one hydrocarbon well, comprising:

instructions of selecting at least one specified well location,

instructions of obtaining at least one time series of top view images ofthe specified well location, in which each top view image is associatedwith a date corresponding to a day of acquisition of the top view image,

instructions of processing the time series of top view images to detectat least one top view image showing the apparition of a well pad and/orshowing drilling activity and/or showing hydraulic fracturing,

instructions of exporting the date corresponding to the day ofacquisition of the top view image showing the apparition of the well padand/or showing drilling activity and/or showing hydraulic fracturing,

instructions of providing, based on the date having been exported, aninformation of pad construction date and/or of drilling starting dateand/or of fracturing starting date and/or a full production forecast forthe specified well location.

According to another aspect of the invention, there is provided a methodfor detecting drilling or hydraulic fracturing of wells, whereinsatellite images obtained from different sources are processed to detectand locate well pads and wherein change of activity on a detected wellpad is followed using machine learning and/or detection algorithms todetect new drilling or fracturing.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be more clearly understood from the followingdescription, given solely by way of non-limiting example in reference tothe appended drawings, in which:

FIG. 1 schematically illustrates an example of a drilling site,

FIG. 2 schematically illustrates an example of a fracking site,

FIG. 3 schematically illustrates an organigram of a method according toan embodiment of the invention,

FIG. 4 shows an example of time series of images retrieved from asatellite,

FIG. 5 schematically illustrates an organigram of a well pad detectionstep of the method according to an embodiment of the invention,

FIGS. 6A, 6B and 6C schematically illustrate an example of an imageprocessed for well pad detection according to an embodiment of theinvention,

FIGS. 6D and 6E schematically illustrate an example of an imageprocessed for well pad focusing according to an embodiment of theinvention,

FIGS. 7A, 7B and 7C schematically illustrate an example of images havingbeen classified according to an embodiment of the invention,

FIG. 8 schematically illustrates a Convolutional Neural network whichmay be used according to an embodiment of the invention,

FIG. 9 schematically illustrates an example of two components of imagescalculated according to an embodiment of the invention,

FIG. 10 schematically illustrates an organigram of a possible sub-stepof filtering images of the method according to an embodiment of theinvention,

FIG. 11 schematically illustrates novelty images calculated according toan embodiment of the invention,

FIG. 12 schematically illustrates an example of possible ranges of datesfor pad appearance, calculated according to an embodiment of theinvention,

FIG. 13 schematically illustrates an organigram of a method according toanother embodiment of the invention,

FIG. 14 schematically illustrates a device for detecting drilling and/orhydraulic fracturing of hydrocarbon wells according to an embodiment ofthe invention,

FIG. 15 schematically illustrates an example of an information providedfor a well pad location.

DETAILED DESCRIPTION OF THE INVENTION

Hereinafter, hydrocarbon wells may be oil wells and/or gas wells. Oiland gas wells share many visible common features. The first one is thewell pad WP, as shown on the FIGS. 1, 2, 6A, 6B, 6D, 6E, 7A, 7B, 7C and15. The well pad WP is a cleared piece of land on which rests the well.The clearing of the land to build a well pad WP always precedes thedrilling or the fracking of the well. During drilling, many objects arevisible on the well pad WP to perform the drilling. These elements areshown on FIG. 1: a drilling rig, water/mud pit, casing pipes, vehiclesof a crew. The well pad WP is a visible bright rectangle of sand.

For the fracking procedure, a significant number of trucks and tanksused to store fracturing fluid become visible, as illustrated on FIG. 2,showing frac pumps, wellheads, sand and frac fluid storage tanks, a datamonitoring van, water trucks, frac blenders, chemical storage trucks.

The method for detecting drilling and/or hydraulic fracturing and/or padconstruction of hydrocarbon wells according to the invention isillustrated on FIG. 3. The method provides a first step S1 of selectingat least one specified well location. This may be done in the USA byselecting at least one specified well location from at least onedatabase of drilling permits. Then an updated database of all thecurrent permits that need to be tracked may be constituted. As mentionedabove, the first stage in the life of a well is a drilling permit thatspecifies a location and an expiration date. The operator that owns thepermit can drill at any time at this location before the expirationdate. Furthermore, in order to test and calibrate the invention,official reports of drilling and fracking activity may be used. Asmentioned above, these reports are lagged but they constitute anothersource of information that may be used to cross-validate the results.Some or all data of the drilling permit, for example the drilling permitdate D1 in FIG. 15, may be provided in the information being outputtedat step S5.

In a second step S2 following step S1, at least one time series of firsttop view images of the specified well location, in which each first topview image is associated with a first date corresponding to a day ofacquisition of the first top view image, are obtained. For example, eachfirst top view image of the time series is associated with a data whichis the day of acquisition of this image. For example, the images andassociated dates are obtained from a media, in which the images and thedata of day of acquisition are registered. The media may be an externalmedia, which may be a database, a distant server, a website, a distantcomputer or others. The media may be a local media, such as memory orstorage media. The top view images are obtained by an image productionmodule, which may be a computer or any automatic machine, able todownload the images and the data of day of acquisition associatedtherewith from the media. For example, each first top view image of thetime series has an identification, such as for example TVI shown on FIG.15, wherein each identification uniquely identifies the first top viewimage and is different from the identification the other first top viewimages. The identification TVI of the first top view image correspondingto the date having been exported may be provided in the informationbeing outputted, as shown on FIG. 15.

The first top view images may be satellite images, aerial images, radarimages. The first top view images may be images taken from above thespecified well location by an image acquisition device, which may be oneor more satellites, using photographic or radar sensors. These top viewimages may also be aerial images, taken from a plane or a drone, usingphotographic or radar sensors. The first top view images are also calledfirst images. The use of multiple satellites improves accuracy of theestimation of the pad creation date.

In order to detect drilling or fracking or pad construction, theinvention uses the time series of first top view images of a specifiedwell location. For example, the first top view images may be satelliteimages or earth observation images. The satellite images may come frommultiple satellites. As of today, there is a wide range of publicallyavailable satellite images. Depending on the satellite and the lens usedto take the picture, images have different types of characteristics.Characteristics of satellite images are:

Spatial resolution of the image:

It gives the corresponding length of one pixel in meters. The lower theresolution, the sharper and more visible the image is.

Spectral band (for optical satellites):

It is the wavelength sensitivity of the sensor of the satellite or thecolors (light wavelengths) that the sensor of the satellite can record.One satellite often carries multiple bands for each image. Mostpublically available bands are in the visible and in the near infrared.For example, band 1 of the satellite Landsat 7 captures the wavelength0.450 to 0.515 μm, which corresponds to the color blue, band 3 thesatellite Landsat 8 corresponds to the red band, band 7 of the satelliteLandsat 8 corresponds to the near infrared. The bands capture detailabout a location differently.

Image file format:

An image is an array where each case corresponds to the value (color) ofa pixel. However, the way these image arrays are saved varies with thesatellite. The most common type of format is the GEOTIFF file format.This file format used by the satellites Landsat 8 and Sentinel2 enablesthe user to perform several operations like cropping around geographicalarea easily. The pixels take values between 0 and 2¹⁶.

The invention may use distant servers for obtaining the first images,for the sourcing of the first images and for the cropping of the firstimages. Thanks to a pre-computation, it may be accessed later in nearreal time to all the available images of any location for any band,crop. The invention may use an Application Programming Interface or APIfor every satellite. These APIs, take as input any location in WSG84 orLatitude/Longitude and return the time series of images for thatlocation. FIG. 4 shows an example of time series of 9 visible images ofsatellite Sentinel2 after request at the API. The location used is thelocation of an oil well in Texas. These images are RGB images (Band 2, 3and 4 combined) display in 8-bits PNG format. The resolution is 10 m.

The second step S2 may include a sub-step S22 of removing non-usablefirst top view images from the time series, made by an imagepre-processing module. To detect drilling or fracking activity or padconstruction, the invention uses times series of satellite images inseveral spectral bands. However, the images returned by the API may notall be usable. For optical images for example, some may be completelyblack and others too cloudy to see anything. An existing open-sourcetoolbox may be used. The goal of the image pre-processing module ismainly to detect which images are covered by clouds and, thus, notusable by the pad detection algorithm. This cloud issue concerns thesatellite Landsat-8 and Sentinel2 because of their visible andnear-infrared bands. However, it is not an issue for Sentinel1 which hasradar images. Sentinel-2 and Landsat-8 images provide their images withcloud masks. For Landsat-8 this information is contained in a specialquality band named BQA whereas for Sentinel2, the cloud mask iscontained in a separate file. These masks give already a first filteringof clouds. FIG. 10 shows that according to an embodiment of theinvention, the step S2 may comprise a sub-step S21 for obtaining by asatellite API a time series of first satellite images for the specifiedwell location. Then, the sub-step S22 of removing non-usable first topview images from the time series (image pre-processing) is carried outon the time series of images. Then, the sub-step S23 determines by analgorithm from the kept first top view images their first date(detection date).

In a third step S3 following step S2, the time series of first top viewimages are processed to detect at least one top view image showing theapparition of a well pad WP and/or showing drilling activity and/orshowing fracturing activity. For example, at least one first top viewimage of the time series of first top view images is processed to detecta location of a well pad WP. The invention carries out an automaticdetection of such apparition in the first images.

In the following operations performed on the time series of first topview images for a given well location, satellite and spectral band aredescribed. The type of algorithms and parameters used may differdepending on the type of satellite or the spectral band. Ultimately, allthe activity results by band and satellite may be aggregated to give ageneral view of the activity at the given location for as many dates aspossible. This aggregation of satellites adds significant value as thedates at which each satellite visits the same location differ.

This detection may be performed using image processing and/or machinelearning algorithms. Image processing algorithms transforms the wayimages display objects so that the determining features of activity areeasily discernable. Then, machine learning algorithms are trained todetect these features. This step S3 may be carried out by an activitydetector of the apparatus.

As mentioned, for a greenfield location, the first sign of activity isthe appearance of a well pad WP. Trucks and tanks needed when frackingor drilling are visible on the well pad WP. Thus, it is detected whetheror not a well pad is present in the first image by a well pad detectionalgorithm and, if so, find its location in the first image. The well padWP is detected and its position is known in step S3. The well pad WP maybe detected as being a rectangle or bright rectangle in the first image.

FIG. 5 shows a scheme on how third step S3 may be carried out accordingto an embodiment of the invention, which is described below in referenceto FIGS. 6A, 6B and 6C. FIG. 6A shows an example of a first imageobtained by step S2 (in a red band) of one well location display ingrayscale color map.

According to this embodiment, step S3 of processing comprisesthresholding in sub-step S32 the first top view image, to obtain abinary image having at least one clear zone Z of several contiguouspixels for pixel levels of the first image being above a prescribedthreshold. The at least one top view image showing the apparition of awell pad containing the specific well location is selected from the topview images whose binary image has at least one clear zone for pixellevels of the top view image being above the prescribed threshold. Thethresholding comprises comparing the intensity (or grey level) of thepixels of the first image to the prescribed threshold. Through thethresholding, every pixel of the first image having an intensity abovethe threshold will become white in the binary image or will have a firstprescribed bright intensity value in the binary image, while every pixelof the first image having an intensity below the threshold will becomeblack in the binary image or with a second prescribed dark intensityvalue lower than the first prescribed bright intensity value in thebinary image. The thresholding of the image for the corresponding bandis made to isolate as much cleared land as possible from the background.For example, from image of FIG. 6A, the binary image of FIG. 6B isobtained by sub-step S32.

Then, in sub-step S33, for each pixel inside each clear zone, a distanceof the pixel to a border B delimiting the clear zone Z is calculated asbeing the shortest distance of the pixel to the pixels of the border B.The higher the distance, the more “central” a point is in the clear zoneZ.

Then, in sub-step S34, a pixel having the highest distance is selectedfrom the pixels being inside the clear zone Z. This selected pixel istaken as being the center C of the clear zone Z. For a list of pixels,the one that is the closest to the center is found. The center C shouldcorrespond to the exact location of the well pad WP.

Then, in sub-step S35, the center C of the clear zone Z is provided asbeing the location of the well pad WP. The position of that center C andthe value of the corresponding distance to the border B is outputted.This value is a good estimation of the well pad size. For example, fromimage of FIG. 6B, the image of FIG. 6C is obtained by sub-steps S33, S34and S35. In FIG. 6C, the center C of the well pad WP returned by thealgorithm would be the position of the point near the center and thevalue of the distance is approximately the size of the well pad WP.

In a fourth step S4 following step S3, a step of exporting the datecorresponding to the day of acquisition of the top view image havingbeen detected as showing the apparition of the well pad and/or showingdrilling activity and/or showing fracturing activity, is carried out.This date may be exported for example by extracting the data of day ofacquisition associated with the image, by a computer, or any automaticmachine.

In a fifth step S5 following step S4, an information of pad constructiondate and/or of drilling starting date and/or of fracturing starting dateand/or a full production forecast for the specified well location isprovided based on the date having been exported. The information may beoutputted on an output unit, which may be for example a display screenof a computer, or of a smartphone or any other man-machine interface.FIG. 15 shows an example of an information provided by step S5, in whicha permit date D1, a pad construction date D2, a drilling starting dateD3, a fracturing starting date D4 and a full production date D5 areoutputted in association with a first top view image showing by a sign(here for example a round point) the well pad location WP. Alsoaggregated geographical data, such as for example an interactive map,may be provided in the information being outputted.

In an embodiment, in the step S3 of processing, first top view images ofthe time series of first top view images are focused on the location ofthe well pad WP, to obtain second images focused on the location of thewell pad WP. In this case, the first top view images may be opticalimages. This step may include ignoring the background of the location ofthe well pad WP in the second images. Knowing exactly where the well padWP is in the image, it becomes possible to filter the possible changesin the time series and focus on the ones that fall in the zone ofinterest. This step produces the second images being centered around thelocation of the well pad WP.

FIG. 6D shows is an example of a first image obtained by step S2 (in aninitial RGB band) of one well location display in grayscale color map,similar to FIG. 6A. Using the well pad detection algorithm, theuninteresting background may be removed to keep the well pad WP for thenext steps, as shown on FIG. 6E showing the image of FIG. 6D having beenprocessed according to step S3.

The first top view images may be optical images or radar images. In thiscase, in the step S3 of processing, top view radar image or top viewaerial image are focused on the drilling activity and/or fracturingactivity, to obtain at least one second image focused on the drillingactivity and/or fracturing activity.

In an embodiment, in the fifth step S5 an activity index for each secondimage may be provided in the information. The activity index may be anumber growing with drilling and/or hydraulic fracturing in the secondimages and is associated with the first date of the first top view imagecorresponding to the second image.

The processing step S3 may be carried out using a machine learningalgorithm and/or a change detection algorithm.

Embodiments of the processing step S3 using machine learning algorithmare described below.

According to an embodiment of the invention, the processing step S3comprises providing a set of third top view images, which are focused onat least one other location of another well pad and which have beenalready classified in classes. For example, these set of third top viewimages may have been manually classified. The comparison step S5 maycomprise a classification between two classes, as described below. Amachine learning algorithm is used to learn to recognize features ofdrilling and/or fracking activity, based on the set of third top viewimages and classes. The features of drilling and/or fracking activitymay be trucks and/or tanks on the well pad WP in the second images andthird top view images.

According to an embodiment of the invention, the set of third top viewimages is a training set of images and comprises:

-   -   third top view images being classified in a first class having a        first label representative of drilling and/or hydraulic        fracturing, and    -   other third top view images being classified in a second class        having a second label not representative of drilling and/or        hydraulic fracturing.

For example, the first label may be “active” or “1”, as shown forexample in the FIG. 7C, in which the several objects OBJ in the well padWP are characteristic of fracking and drilling. For example, the secondlabel may be “inactive” or “0”, as shown for example in the FIGS. 7A and7B, in which no object OBJ is present in the well pad WP.

In another example, the first label may be “drilling” or “1”, in whichthe several objects OBJ in the well pad WP are characteristic ofdrilling. For example, the second label may be “fracking” or “0”, inwhich the several objects OBJ in the well pad WP are characteristic offracking.

A machine learning algorithm is used for classifying the second imagesbetween the first class and the second class, based on at least thethird top view images having been classified, to calculate for eachsecond image the activity index of the second image.

According to an embodiment of the invention, the activity index is areal number not lower than zero and not higher than one and whichrepresents a predicted probability p_(i) that the second image belongsto the first class representative of drilling and/or hydraulicfracturing.

In a variant, the first class may be instead a first class having afirst label representative of pad construction and the second class maybe a second class having a second label not representative of padconstruction, for third top view images focused on at least one otherlocation of drilling activity and/or fracturing activity instead of atleast one other location of another well pad.

According to an embodiment of the invention, the machine learningalgorithm may comprise using at least one of Histogram of gradients withLinear model, Support Vector Machine, K-Nearest neighbor's algorithm,Random Forests, Support Vector Machine with kernel, Support VectorMachine without kernel, Neural Networks, Convolutional Neural networks.

According to an embodiment of the invention, the machine learningalgorithm comprises using a Convolutional Neural network to calculatefor each second image the activity index of the second image. TheConvolutional Neural network may comprise three convolutional layers asshown on FIG. 8, and a neural net layer (fully connected layer). Eachconvolutional layers may have a pooling of 2×2, and activation being themaximum in view of zero.

According to an embodiment of the invention, the machine learningalgorithm comprises training of the Convolutional Neural network tominimize an objective function calculated as

${\log{loss}} = {{- \frac{1}{N}}{\sum\limits_{i = 1}^{N}( {{y_{i}{\log( p_{i} )}} + {( {1 - y_{i}} ){\log( {1 - p_{i}} )}}} )}}$whereN is the number of second images,i are indexes of the second images and go from 1 to N,y_(i) is the label of the second image of index i and is equal to thefirst label being 1 for the first class and being equal to the secondlabel being 0 for the second class.

According to another embodiment of the invention, the processing step S3comprises providing a set of third top view images, which are focused onat least one other location of another well pad WP. According to anembodiment of the invention, the processing step S3 may comprise aclassification between more than two classes.

For example, the set of third top view images comprises:

-   -   third top view images being classified in a first class having a        first label representative of drilling and/or hydraulic        fracturing,    -   third top view images being classified in a second class having        a second label representative of a presence of the well pad WP,    -   third top view images being classified in a third class having a        third label representative of micro activity spotted, and    -   third top view images being classified in a fourth class having        a fourth label not representative of drilling, hydraulic        fracturing, presence of the well pad WP, micro activity spotted.    -   using a machine learning algorithm, for classifying the second        images between the first class, the second class, the third        class and the fourth class, based on at least the third top view        images being classified.

According to an embodiment of the invention, the machine learningalgorithm comprises using a Convolutional Neural network to calculatefor each second image the activity index of the second image, which is areal number not lower than zero and not higher than one and whichrepresents a predicted probability p_(i,k) that the second image belongsto the first class, second class, third class and fourth class.According to an embodiment of the invention, the machine learningalgorithm comprises training of the Convolutional Neural network tominimize an objective function calculated as

${{multiClass}\;{{Log}{loss}}} = {{- \frac{1}{N}}{\sum\limits_{i = 1}^{N}{\sum\limits_{k \in {Labels}}^{\;}{y_{i,k}{\log( p_{i,k} )}}}}}$

where

N is the number of second images,

i are indexes of the second images and go from 1 to N,

k is a variable designating the first label, second label, third labeland fourth label,

y_(i,k) is a binary variable indicating if the second image i is oflabel k.

Once trained and tested, the machine learning algorithm can be used onany new location to analyze the different time series per satellite and,by aggregating the results, an overview of what happened on the givenlocation can be obtained.

According to another embodiment of the invention, the processing step S3comprises detecting changes representative of pad construction(especially for second images focused on a well pad) and/or drillingand/or hydraulic fracturing in the second images (especially for secondimages focused on a drilling activity and/or fracturing activity).

The detected change is provided in the information being outputted.Embodiments of the processing step S3 using detecting changes aredescribed below.

The time series of second images may be projected in a space where anyunusual event would correspond to activity. This space may be made ofseveral statistical properties of the time series.

According to an embodiment of the invention, the processing step S3comprises projecting each second image on an histogram calculated forthe second images, to detect changes representative of pad construction(especially for second images focused on a well pad) and/or drillingand/or hydraulic fracturing in the second images (especially for secondimages focused on a drilling activity and/or fracturing activity).

According to another embodiment of the invention, the processing step S3comprises a dimensionality reduction technique. For example, theprocessing step S3 comprises using a kernel PCA (principal componentanalysis) on the cropped time series (second images). For example, thecomparison step S5 comprises projecting each second image on aneigenvector calculated from is the covariance matrix of the secondimages, wherein the activity index of the second images may be thecomponent of the second image along the eigenvector. According to anembodiment of the invention, the comparison step S5 comprises using aprincipal component analysis, which comprises calculating eigenvaluesand eigenvectors of a matrix C, which is the covariance matrix of thesecond images x_(j) in a feature space Φ(x_(j)), according to

$C = {\frac{1}{l}{\sum\limits_{j = 1}^{l}{{\phi( x_{j} )} \cdot {\phi( x_{j} )}^{t}}}}$

where

l is the number of second images x_(j),

j is number going from 1 to l, and

calculating the activity index from the eigenvalues and theeigenvectors. The activity index is provided in the information beingoutputted.

For a time series of cropped images (second images), each second imagemay be into a 2D space, as shown for example on FIG. 9. On FIG. 9, aseparation (illustrated by the boundary line) between the images withactivity and the ones with no activity is present. Detecting activitythen becomes a simple value test: if the point is above the boundary,there is activity on the well (points indicated as drilling, microactivity and fracking on FIG. 9). The kernel used in FIG. 9 is a4-degree polynomial kernel with two components.

According to another embodiment of the invention, the processing step S3comprises detecting changes between the second images in the timeseries.

According to an embodiment of the invention, the processing step S3comprises a Novelty features algorithm over the time series of secondimages in order to find what is new from one image to another andFeatures engineering over the novelties to define the keycharacteristics that make a novelty a pad appearance and not a randomevent, and a Machine learning algorithm trained on the engineeredfeatures over a labelled train set. The detected change is provided inthe information being outputted.

According to an embodiment of the invention, the processing step S3comprises:

-   -   calculating for each second image Z a set of real numbers x_(i)        in order to

$\underset{x_{1},\mspace{11mu}\ldots\mspace{11mu},{x_{n} \in {\mathbb{R}}}}{minimize}{{Z - {\sum\limits_{i = 1}^{n}{x_{i}Y_{i}}}}}^{2}$subject   to  x_(i) ≥ 0i ∈ [1, n],and then calculating for each second image Z a novelty image R accordingto:

$R = {Z - {\sum\limits_{i = 1}^{N}{x_{i}^{*}{Yi}}}}$

where Y_(i) designates the second images which correspond to a firstimage associated with a first date being prior to the first date of thefirst image or images corresponding to the second image Z for i goingfrom 1 to n,

x_(i)* designates the set of real numbers x_(i) having been calculated,and

-   -   calculating the activity index from the novelty images R. For        every given second image, the novelty feature algorithm tries to        explain locally what is visible thanks to the available past        second images Y_(i).

FIG. 11 shows an example of the result of the novelty features algorithmapplied on a time series of Landsat-8 band 8 images (second images). Thesecond images are on the upper row and their corresponding noveltyimages are on the bottom row. For novelty images, new objects comparedto the past have a high pixel value. The pad appearance may be seen inthe second novelty image. Through the novelty features algorithm, thesecond images are pre-processed to display novelty in the time series.

Features engineering may comprise extracting specific features that helpdefine what is a pad appearance compared to random event. This may bemade based on a prior knowledge of the shape, the placing and thetexture of well pads.

A machine learning algorithm similar to the ones mentioned above may beused on the features obtained by the Features engineering.

According to an embodiment of the invention, a range of dates indicatingpad construction and/or drilling and/or hydraulic fracturing and/or afull production forecast is associated with the information beingoutputted. For every well pad detected, the actual pad appearance dateis in between the detection date and the previous image date in the timeseries. Since different satellites provide time series of images withdifferent dates, the results of the algorithm may be combined to limitthe possible error made, for example as shown in FIG. 12. FIG. 12 showspossible ranges of dates for pad appearance for a permian permit. Bycombining detection coming from a first satellite (Satellite 1) timeseries and a second satellite (Satellite 2) time series on FIG. 12, thegap between the previous image and the detection image is reduced to 19days. The below table 1 shows as an example for FIG. 12 the mean maxerror possibly made due to the gap between the detection image and theprevious image over 1254 pad appearances in the Permian basin.

TABLE 1 Satellite Mean max error (days) Satellite 1 20.0 Satellite 227.5 Combined 12.1

According to an embodiment of the invention, the first top view imagesare drone images. The drone images are aerial images taken by a drone.

In an embodiment, the pad construction/drilling/fracturing date for aspecific well obtained through this method can then be used to estimatea first date of production for the well. Using public or proprietaryproduction data of the wells in proximity, geologic data, or other datacontaining parameters that influence well production, a full productionprofile for the well can be obtained to forecast production. One exampleis the time calibration of decline curves according to observed padconstruction/drilling/fracturing dates observed through the invention.In order to obtain the first date of production for a specified well,one example is to use historical data of detections through the methodand historical production data from public databases. Statistics on thisdata can give a minimum and average time between drilling/hydraulicfracturing and first production, that can be applied to futuredetections.

As shown on FIG. 14, the invention provides also a device 100 orapparatus 100 detecting pad construction for at least one hydrocarbonwell and/or for detecting drilling of at least one hydrocarbon welland/or for detecting hydraulic fracturing of at least one hydrocarbonwell, comprising:

a selector module 101 for selecting at least one specified welllocation,

an image production module 102 for obtaining at least one time series oftop view images of the specified well location, in which each top viewimage is associated with a date corresponding to a day of acquisition ofthe top view image,

a processing module 103 for processing the time series of top viewimages to detect at least one top view image showing the apparition of awell pad and/or showing drilling activity and/or showing hydraulicfracturing,

an export module 104 for exporting the date corresponding to the day ofacquisition of the top view image showing the apparition of the well padand/or showing drilling activity and/or showing hydraulic fracturing,

an information outputting module 105 for providing, based on the datehaving been exported, an information of pad construction date and/or ofdrilling starting date and/or of fracturing starting date and/or a fullproduction forecast for the specified well location.

The device 100 and the modules 101, 102, 103, 104 and 105 may beembodied by any means, such as for example computers, calculators,processors, microprocessors, permanent memories, servers, databases,computer programs, man-machine interfaces, user interfaces. The device100 may comprises the above-mentioned means to carry out the stepsmentioned above of the method of the invention. A computer programaccording to the invention may comprise instructions for executing thesteps of the method. The computer program may be recorded on any storagemedia, which may be permanent storage memory or a non-permanent storagememory.

According to an embodiment of the invention, the method uses both one orseveral of the machine learning approach mentioned below to calculate asactivity index a first activity index, and the change detection approachmentioned above to calculate as activity index a second activity index.In this case, a further averaging step may be provided, in which anaverage of the first activity index and of the second activity index iscalculated, as shown for example in FIG. 13. This enables to build astronger activity index.

The invention enables to build a new well database with a significantlylower lag using satellite images to detect drilling or frackingactivity. The invention enables to have for every permit tracked a closeto real time view on its pre-drilling/fracking activity. The inventionmay use free and open source data: free satellite images and open sourcewell permit data. The invention automatically gives a temporal activityindex for any given location. The invention considerably reduces the lagof frack and drill reports of wells. The lag of the invention targets atless than a week whereas Texas official databases are more than a yearlagged. The up-to-date database has value for oil and gas investors aswell as analysts.

Of course, the aspects, embodiments, features and examples of theinvention mentioned above may be combined one with another or may beselected independently one from another.

The invention claimed is:
 1. A method for detecting at least one of padconstruction-drilling, or hydraulic fracturing of at least onehydrocarbon well, comprising: selecting at least one specified welllocation, obtaining at least one time series of top view images of thespecified well location, in which each top view image is associated witha date corresponding to a day of acquisition of the top view image,processing the time series of top view images to detect at least one topview image showing at least one of an apparition of a well pad, drillingactivity, or showing fracturing activity, exporting the datecorresponding to the day of acquisition of the top view image showingthe at least one of the apparition of the well pad, the drillingactivity, or the fracturing activity, and providing, based on the datehaving been exported, information of at least one of pad constructiondate, drilling starting date, fracturing starting date, drilling enddate, fracturing end date, or a full production forecast for thespecified well location, wherein the top view images are optical images,wherein the processing comprises focusing the at least one top viewimage showing the apparition of the well pad on the well pad having beendetected, to obtain at least one second image focused on the well padproviding a set of third top view images, which are focused on at leastone other location of another well pad and which comprise third top viewimages being classified in a first class having a first labelrepresentative of pad construction and third top view images beingclassified in a second class having a second label not representative ofpad construction, and using a machine learning algorithm, forclassifying the second images between the first class and the secondclass, based on at least the third top view images being classified, tocalculate for each second image an activity index of the second image,which is a real number not lower than zero and not higher than one andwhich represents a predicted probability p_(i) that the second imagebelongs to the first class, and wherein the activity index is associatedwith the information being provided.
 2. The method according to claim 1,wherein the selecting at least one specified well location comprisesselecting at least one specified well location from at least onedatabase of drilling permits.
 3. The method according to claim 1,wherein the top view images are at least one of satellite images, aerialimages, or radar images taken from a satellite.
 4. The method accordingto claim 1, wherein at least one of: (i) data of a drilling permit, (ii)aggregated geographical data, and (iii) an interactive map is associatedwith the information being provided.
 5. The method according to claim 1,wherein the processing comprises detecting the at least one top viewimage showing the apparition of the well pad as being a top view imagehaving, as the well pad, a rectangle.
 6. The method according to claim1, wherein the processing comprises thresholding the top view images inview of pixel levels of the top view image being above a prescribedthreshold, to obtain binary images, wherein the at least one top viewimage showing the apparition of the well pad containing the specificwell location is selected from the top view images whose binary imagehas at least one clear zone for pixel levels of the top view image beingabove the prescribed threshold.
 7. The method according to claim 6,wherein the processing comprises for each pixel inside each clear zone,calculating a distance of the pixel to a border delimiting the clearzone, being the shortest distance of the pixel to the pixels of theborder, and selecting from the pixels being inside the clear zone apixel having the highest distance as being a center of the clear zone,wherein the center of the clear zone as a location of the well pad isassociated with the information being provided.
 8. The method accordingto claim 7, wherein the distance of the center as being an estimation ofa size of the well pad is associated with the information beingprovided.
 9. The method according to claim 1, wherein a range of datesindicating at least one of pad construction, drilling, hydraulicfracturing, or a full production forecast is associated with theinformation being provided.
 10. The method according to claim 1, whereinthe machine learning algorithm comprises using at least one of: ahistogram of gradients with a linear model, a support vector machine, ak-nearest neighbor's algorithm, random forests, a support vector machinewith a kernel, a support vector machine without a kernel, neuralnetworks, or convolutional neural networks.
 11. The method according toclaim 1, wherein the machine learning algorithm comprises using aconvolutional neural network configured to calculate for each secondimage the activity index of the second image, with training of theconvolutional neural network to minimize an objective functioncalculated as${logloss} = {{- \frac{1}{N}}{\sum\limits_{i = 1}^{N}( {{y_{i}{\log( p_{i} )}} + {( {1 - y_{i}} ){\log( {1 - p_{i}} )}}} )}}$where N is the number of second images, i are indexes of the secondimages and go from 1 to N, y_(i) is the label of the second image ofindex i and is equal to the first label being 1 for the first class andbeing equal to the second label being 0 for the second class.
 12. Themethod according to claim 1, wherein the processing comprises detectingat least one change representative of pad construction in several secondimages, wherein the at least one change having been detected isassociated with the information being provided.
 13. The method accordingto claim 1, wherein the processing comprises calculating a histogram forseveral second images and comparing each of the second images to thehistogram, to detect at least one change representative of padconstruction in the second images, wherein the at least one changehaving been detected is associated with the information being provided.14. The method according to claim 1, wherein the processing comprises:using a principal component analysis, which comprises calculatingeigenvalues and eigenvectors of a matrix C, which is the covariancematrix of several second images x_(j) in a feature space Φ(x_(j)),according to$C = {\frac{1}{l}{\sum\limits_{j = 1}^{l}{{\phi( x_{j} )} \cdot {\phi( x_{j} )}^{t}}}}$where l is the number of second images x_(j), and j is number going from1 to l, and calculating an activity index from the eigenvalues and theeigenvectors, wherein the activity index is associated with theinformation being provided.
 15. The method according to claim 1, whereinthe processing comprises detecting at least one change between severalsecond images in the time series, wherein the at least one change havingbeen detected is associated with the information being provided.
 16. Adevice for detecting at least one of pad construction, drilling, orhydraulic fracturing of at least one hydrocarbon well, the devicecomprising: a processor operably coupled to a memory and configured to:select at least one specified well location, obtain at least one timeseries of top view images of the specified well location, in which eachtop view image is associated with a date corresponding to a day ofacquisition of the top view image, process the time series of top viewimages to detect at least one top view image showing at least one of anapparition of a well pad, drilling activity, or hydraulic fracturing,focus the at least one top view image showing the apparition of the wellpad on the well pad having been detected, to obtain at least one secondimage focused on the well pad, wherein the top view images are opticalimages, provide a set of third top view images, which are focused on atleast one other location of another well pad and which comprise thirdtop view images being classified in a first class having a first labelrepresentative of pad construction and third top view images beingclassified in a second class having a second label not representative ofpad construction, use a machine learning algorithm, for classifying thesecond images between the first class and the second class, based on atleast the third top view images being classified, to calculate for eachsecond image an activity index of the second image, which is a realnumber not lower than zero and not higher than one and which representsa predicted probability p_(i) that the second image belongs to the firstclass, export the date corresponding to the day of acquisition of thetop view image showing the at least one of the apparition of the wellpad, the drilling activity, or the hydraulic fracturing, and provide,based on the date having been exported, at least one of information ofpad construction date, drilling starting date, fracturing starting date,or a full production forecast for the specified well location, whereinthe activity index is associated with the information being provided.17. A non-transitory computer-readable storage medium havinginstructions stored thereon that, when executed by a processor, causethe processor to execute operations for detecting at least one of padconstruction, drilling, or hydraulic fracturing of at least onehydrocarbon well, the operations comprising: selecting at least onespecified well location, obtaining at least one time series of top viewimages of the specified well location, in which each top view image isassociated with a date corresponding to a day of acquisition of the topview image, processing the time series of top view images to detect atleast one top view image showing at least one of an apparition of a wellpad, drilling activity, or hydraulic fracturing, exporting the datecorresponding to the day of acquisition of the top view image showingthe at least one of the apparition of the well pad, the drillingactivity, or the hydraulic fracturing, providing, based on the datehaving been exported, information of at least one of pad constructiondate, drilling starting date, fracturing starting date, or a fullproduction forecast for the specified well location, wherein the topview images are optical images, wherein the processing comprisesfocusing the at least one top view image showing the apparition of thewell pad on the well pad having been detected, to obtain at least onesecond image focused on the well pad, providing a set of third top viewimages, which are focused on at least one other location of another wellpad and which comprise third top view images being classified in a firstclass having a first label representative of pad construction and thirdtop view images being classified in a second class having a second labelnot representative of pad construction, and using a machine learningalgorithm, for classifying the second images between the first class andthe second class, based on at least the third top view images beingclassified, to calculate for each second image an activity index of thesecond image, which is a real number not lower than zero and not higherthan one and which represents a predicted probability p_(i) that thesecond image belongs to the first class, and wherein the activity indexis associated with the information being provided.
 18. A method fordetecting at least one of pad construction-drilling, or hydraulicfracturing of at least one hydrocarbon well, comprising: selecting atleast one specified well location, obtaining at least one time series oftop view images of the specified well location, in which each top viewimage is associated with a date corresponding to a day of acquisition ofthe top view image, processing the time series of top view images todetect at least one top view image showing at least one of an apparitionof a well pad, drilling activity, or showing fracturing activity,exporting the date corresponding to the day of acquisition of the topview image showing the at least one of the apparition of the well pad,the drilling activity, or the fracturing activity, and providing, basedon the date having been exported, information of at least one of padconstruction date, drilling starting date, fracturing starting date, ora full production forecast for the specified well location, wherein thetop view images are optical images or radar images, and wherein theprocessing comprises focusing the at least one top view radar image ortop view aerial image showing the apparition of at least one of drillingactivity or fracturing activity, to obtain at least one second imagefocused on the at least one of the drilling activity or the fracturingactivity, providing a set of third top view images, which are focused onat least one other location of at least one of drilling activity orfracturing activity and which comprise third top view images beingclassified in a first class having a first label representative of atleast one of drilling or hydraulic fracturing and third top view imagesbeing classified in a second class having a second label notrepresentative of the at least one of the drilling or the hydraulicfracturing, and using a machine learning algorithm, for classifying thesecond images between the first class and the second class, based on atleast the third top view images being classified, to calculate for eachsecond image an activity index of the second image, which is a realnumber not lower than zero and not higher than one and which representsa predicted probability p_(i) that the second image belongs to the firstclass, and wherein the activity index is associated with the informationbeing provided.
 19. The method according to claim 18, wherein themachine learning algorithm comprises using at least one of: a histogramof gradients with a linear model, a support vector machine, a k-nearestneighbor's algorithm, random forests, a support vector machine with akernel, a support vector machine without a kernel, neural networks, orconvolutional neural networks.
 20. The method according to claim 18,wherein the machine learning algorithm comprises using a convolutionalneural network configured to calculate for each second image theactivity index of the second image, with training of the convolutionalneural network to minimize an objective function calculated as${logloss} = {{- \frac{1}{N}}{\sum\limits_{i = 1}^{N}( {{y_{i}{\log( p_{i} )}} + {( {1 - y_{i}} ){\log( {1 - p_{i}} )}}} )}}$where N is the number of second images, i are indexes of the secondimages and go from 1 to N, y_(i) is the label of the second image ofindex i and is equal to the first label being 1 for the first class andbeing equal to the second label being 0 for the second class.
 21. Themethod according to claim 18, wherein the processing comprises detectingat least one change representative of at least one of drilling orhydraulic fracturing in several second images, wherein the at least onechange having been detected is associated with the information beingprovided.
 22. The method according to claim 18, wherein the processingcomprises projecting each second image on a histogram calculated forseveral second images, to detect at least one change representative ofat least one of drilling or hydraulic fracturing in the second images,wherein the at least one change having been detected is associated withthe information being provided.
 23. The method according to claim 18,wherein the processing comprises: using a principal component analysis,which comprises calculating eigenvalues and eigenvectors of a matrix C,which is the covariance matrix of several second images x_(j) in afeature space Φ(x_(j)), according to$C = {\frac{1}{l}{\sum\limits_{j = 1}^{l}{{\phi( x_{j} )} \cdot {\phi( x_{j} )}^{t}}}}$where l is the number of second images x_(j), and j is number going from1 to l, and calculating an activity index from the eigenvalues and theeigenvectors, wherein the activity index is associated with theinformation being provided.
 24. The method according to claim 18,wherein the processing comprises detecting at least one change betweenseveral second images in the time series, wherein the at least onechange having been detected is associated with the information beingprovided.
 25. A device for detecting at least one of pad construction,drilling, or hydraulic fracturing of at least one hydrocarbon well, thedevice comprising: a processor operably coupled to a memory andconfigured to: select at least one specified well location, obtain atleast one time series of top view images of the specified well location,in which each top view image is associated with a date corresponding toa day of acquisition of the top view image, wherein the top view imagesare optical images or radar images, and process the time series of topview images to detect at least one top view image showing at least oneof an apparition of a well pad, drilling activity, or hydraulicfracturing, wherein the processing comprises focusing the at least onetop view radar image or top view aerial image showing the apparition ofat least one of drilling activity or fracturing activity, to obtain atleast one second image focused on the at least one of the drillingactivity or the fracturing activity providing a set of third top viewimages, which are focused on at least one other location of at least oneof drilling activity or fracturing activity and which comprise third topview images being classified in a first class having a first labelrepresentative of at least one of drilling or hydraulic fracturing andthird top view images being classified in a second class having a secondlabel not representative of the at least one of the drilling or thehydraulic fracturing, and using a machine learning algorithm, forclassifying the second images between the first class and the secondclass, based on at least the third top view images being classified, tocalculate for each second image an activity index of the second image,which is a real number not lower than zero and not higher than one andwhich represents a predicted probability p_(i) that the second imagebelongs to the first class, and wherein the activity index is associatedwith the information being provided.
 26. A non-transitorycomputer-readable storage medium having instructions stored thereonthat, when executed by a processor, cause the processor to executeoperations for detecting at least one of pad construction, drilling, orhydraulic fracturing of at least one hydrocarbon well, the operationscomprising: selecting at least one specified well location, obtaining atleast one time series of top view images of the specified well location,in which each top view image is associated with a date corresponding toa day of acquisition of the top view image, processing the time seriesof top view images to detect at least one top view image showing atleast one of an apparition of a well pad, drilling activity, orhydraulic fracturing, exporting the date corresponding to the day ofacquisition of the top view image showing the at least one of theapparition of the well pad, the drilling activity, or the hydraulicfracturing, and providing, based on the date having been exported,information of at least one of pad construction date, drilling startingdate, fracturing starting date, or a full production forecast for thespecified well location, wherein the top view images are optical imagesor radar images, wherein the processing comprises focusing the at leastone top view radar image or top view aerial image showing the apparitionof at least one of drilling activity or fracturing activity, to obtainat least one second image focused on the at least one of the drillingactivity or the fracturing activity, providing a set of third top viewimages, which are focused on at least one other location of at least oneof drilling activity or fracturing activity and which comprise third topview images being classified in a first class having a first labelrepresentative of at least one of drilling or hydraulic fracturing andthird top view images being classified in a second class having a secondlabel not representative of the at least one of the drilling or thehydraulic fracturing, and using a machine learning algorithm, forclassifying the second images between the first class and the secondclass, based on at least the third top view images being classified, tocalculate for each second image an activity index of the second image,which is a real number not lower than zero and not higher than one andwhich represents a predicted probability p_(i) that the second imagebelongs to the first class, and wherein the activity index is associatedwith the information being provided.